Adaptive control system

ABSTRACT

An adaptive control system for reducing undesired signals comprises sensors (31) to provide signals indicative of the undesired signals, and a processor (36) which processes the first signal to provide a secondary signal for output to sources (37) to interfere with the undesired signals. Sensors (42) are provided to detect the residual signals which are indicative of the interference between the undesired and secondary signals. Within the processor the signals indicative of the undesired signals and the residual signals are transformed into the frequency domain and collated. The outcome of the collation is inverse transformed and the processor adjusts the secondary signal using this inverse transform to reduce the residual signal from the sensors (42).

BACKGROUND OF THE INVENTION

The present invention relates to an adaptive control system and methodfor reducing undesired primary signals generated by a primary source ofsignals.

The basic principle of adaptive control is to monitor the primarysignals and produce a cancelling signal which interfers destructivelywith the primary signals in order to reduce them. The degree of successin cancelling the primary signals is measured to adapt the cancellingsignal to increase the reduction in the undesired primary signals.

This idea is thus applicable to any signals such as electrical signalswithin an electrical circuit in which undesired noise is produced. Oneparticular area which uses such adaptive control is in the reduction ofunwanted acoustic vibrations in a region.

It is to be understood that the term "acoustic vibration" applies to anyacoustic vibration including sound.

There has been much work performed in this area with a view to providinga control system which can adapt quickly to changes in amplitude andfrequency of vibrations from a source. Prior art adaptive controlsystems either operate in the time or frequency domain on the drivesignal to be output to cancel the noise. A time domain system isdisclosed in WO88/02912. In this document a controller is disclosedwhich is implemented as a digital adaptive finite impulse response (FIR)filter. In order for the filter to be adapted the filter coefficentsmust be modified based on the degree of success in cancelling theundesired vibrations. For such a control system disclosed in thisdocument, where there are a large number of error signals, drive signalsand reference signals, there are a large number of calculations whichmust be performed for each update of the coefficients. For instance, anestimate of the response of each sensor to each drive signal (the Cfilter) must be taken into consideration in the calculation of theupdate of the filter coefficients.

WO88/02912 also discloses the operation of a digital filter in thefrequency domain. Such a filter has complex filter coefficients andrequires the reference signal and error signals to be transformed intothe frequency domain and the output drive signal from the adaptivefilter to be inverse transformed back to the time domain in order toprovide the drive signal. The transform which is conveniently used isthe Fourier transform. In order for such a transform to be performed anumber of data points within a window length are transformed and used toadapt the following window of data. Such a discrete Fourier transformprovides good control if the length of the window (or number of datapoints) is long, but this provides a long delay in the update. A shortwindow of data on the other hand provides for a quick adaption but poorcontrol.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide an adaptive controlsystem which is computationally efficient compared with time domainadaptive control systems and which overcomes the problems associatedwith frequency domain adaptive control systems.

The present invention provides an adaptive control system for reducingundesired signals, comprising signal means to provide at least one firstsignal indicative of at least selected undesired signals; processingmeans adapted to use said at least one first signal to provide at leastone secondary signal to interfere with the undesired signals; andresidual means to provide for said processing means at least oneresidual signal indicative of the interference between said undesiredand secondary signals; wherein said processing means is adapted totransform said at least one first signal and said at least one residualsignal to provide the amplitude and phase of spectral components of saidsignals, to collate the transformed signals, to inverse transform of theoutcome of said collation, and to adjust the or each secondary signalusing the inverse transform of the outcome of the collation to reducesaid at least one residual signal.

Preferably said processing means comprises adaptive response filtermeans having filter coefficients and adapted to adjust the oreach-secondary signal using said filter coefficients to reduce the oreach residual signals, and to modify the filter coefficients using saidinverse transform of the outcome of the collation.

Also preferably said processing means is adapted to collate saidtransformed signals by forming at least one cross spectral estimate, toinverse transform said at least one cross spectral estimate, to form atleast one cross correlation estimate and to modify the filtercoefficients of said adaptive response filter using said at least onecross correlation estimate.

Preferably the processing means is adapted to digitally sample said atleast one first signal and said at least one residual signal, and tostore a plurality of digits for each said signal to form first signaldata blocks and residual signal data blocks respectively, said firstsignal data blocks and said residual signal data blocks being timealigned; said processing means being further adapted to set a number ofsaid digits at the end of each first signal data block to zero to form amodified first signal data block, and to transform the modified firstsignal data block and the associated residual signal data block to usein the collation.

Preferably the number of digits at the end of each modified first signaldata block which is set to zero depend on the delay between the firstsignal and the contribution from the first signal in the residualsignal. The number of digits set to zero are preferably selected suchthat the time taken to sample said number is greater than the delayexperienced by a signal passing through said adaptive response filter.

Preferably the cross spectral estimate is formed by multiplying thecomplex conjugate of the transform of the first signal with thetransform of the residual signal.

Preferably the transform performed on the first signal and the residualsignal is the Fourier transform although any transform could be used inwhich the cross talk between frequencies is minimal or non-existent.

In order to control the stability of the adaptive control, preferablythe cross spectral estimate is multiplied with a convergence coefficientwhich is sufficiently small to smooth out the effect of random errors inthe cross spectral estimate on the adaption. Alternatively the crosscorrelation estimate is multiplied with a convergence coefficientsufficiently small to smooth out the effect of random errors in thecross correlation estimate on the adaption.

In one embodiment of the present invention the processing means includessystem response filter means to model the response of the signals fromsaid residual means to at least one secondary signal. In this embodimentsaid system response filter means preferably comprises complex filtercoefficients which are an estimate of the frequency response of saidresidual signals to at least one said secondary signals, and saidprocessing means is adapted to filter the transform of said at least onefirst signal using said complex filter coefficients.

In an alternative embodiment of the present invention the processingmeans includes system response filter means which comprises complexfilter coefficients which are an estimate of the amplitude and anestimate of the inverse of the phase of the frequency response of saidresidual signals to at least one secondary signal, and said processingmeans is adapted to filter the transform of said at least one residualsignal using said complex filter coefficients.

In another embodiment of the present invention the processing means isadapted to modify said filter coefficients to reduce the amplitude ofportions of the or each drive signal by a predetermined amount. Thisaction on the filter coefficients can be termed "effort weighting" andis used to control the stability of the adaptive response filter.

Preferably said residual means provides a plurality of residual signalsand said processing means is adapted to modify said filter coefficientsof said adaptive response filter to reduce the sum of the mean of thesquare of the residual signals.

In one embodiment wherein said undesired signals comprise undesiredacoustic vibrations, said adaptive control system comprises at least onesecondary vibration source responsive to said at least one secondarysignal to provide secondary vibrations to interfere with said undesiredacoustic vibrations; said residual means comprising at least one sensormeans to sense the residual vibrations resulting from the interferencebetween said undesired acoustic vibrations and said secondary vibrationsand to provide said at least one residual signal.

The present invention also provides a method of actively reducingundesired signals, comprising the steps of providing at least one signalindicative of at least selected undesired signals using said at leastone first signal to provide at least one secondary signal to interferewith said undesired signals; providing at least one residual signalindicative of the interference between said undesired and secondarysignals; transforming said at least one first signal and said at leastone residual signal to provide the amplitude and phase of spectralcomponents of said signals, collating the transformed signals; inversetransforming the outcome of the collation and using the inversetransform of the output of the collation to adapt the or each secondarysignal to reduce the residual signals.

In another aspect the present invention provides an adaptive controlsystem for reducing undesired signals, comprising signal means toprovide at least one first signal indicative of at least selectedundesired signals; processing means adapted to use said at least onefirst signal to provide at least one secondary signal to interfere withthe undesired signals; and residual means to provide for said processingmeans at least one residual signal indicative of the interferencebetween said undesired and secondary signals; wherein said processingmeans is adapted to digitally sample said at least one first signal andsaid at least one residual signal; to store a plurality of digits foreach said signal to form first signal and residual signal data blocksrespectively, said first signal data blocks and said residual signaldata blocks being time aligned; to set a number of said digits at theend of each first signal data block to zero to form a modified firstsignal data block, to transform the modified first signal data block andthe residual signal data block to provide the amplitude and phase ofspectral components of said signals, and to adjust the amplitude andphase of spectral components of said at least one secondary signal usingsaid transformed signals to reduce said at least one residual signal.

In a further aspect the present invention provides a method of activelyreducing undesired signals comprising the steps of providing at leastone first signal indicative of at least selected undesired signals;using said at least one first signal to provide at least one secondarysignal to interfere with said undesired signals; providing at least oneresidual signal indicative of the interference between said undesiredand secondary signals; digitally sampling said at least one first signaland said at least one residual signal; storing a plurality of digits foreach said signal to form first signal and residual signal data blocks,said first signal and residual signal data blocks being time aligned;setting a number of said digits at the end of each first signal datablock to zero to form a modified first signal data block, transformingthe modified first signal data block to provide the amplitude and phaseof spectral components of said signals, and adjusting the amplitude andphase of spectral components of said at least one secondary signal usingsaid transformed signals to reduce said at least one residual signal.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of the present invention will now be described with referenceto the drawings, in which:

FIGS. 1a and 1b illustrate schematically alternative adaptive controlsystems according to embodiments of the present invention;

FIG. 1c illustrates an expansion of the arrangement shown in FIG. 1a fortwo reference signals;

FIG. 1d illustrates an expansion of the arrangement shown in FIG. 1a fortwo error sensors; and

FIG. 1e illustrates an expansion of the arrangement shown in FIG. 1a fortwo secondary vibration sources;

FIG. 2 illustrates the blocks of reference and error signal data usedfor the transform to form the cross spectral estimate;

FIG. 3 is a schematic drawing of an active vibration control system forpractical implementation; and

FIGS. 4a and 4b illustrate schematically frequency domain adaptivecontrol systems in accordance with embodiments of the present invention.

Referring now to the drawings, 1a and 1b illustrate alternative adaptivecontrol systems which can be used in accordance with the presentinvention. Both FIGS. 1a and 1b illustrate a single channel systemhaving a single reference signal x(n) which represents the signal from asensor, and a single output y(n) from the w filter which represents thedrive signal to a secondary vibration source. e(n) represents the errorsignal indicative of the residual vibrations after interference betweenthe primary and secondary vibrations. The single channel system is shownfor simplicity although the present invention is equally applicable tothe multichannel system where the Fourier transform of each referencesignal x(n) must be taken as well as the Fourier transform of each errorsignal e(n).

In FIGS. 1a and 1b, A represents the acoustic response of the pathwayfrom the primary source of vibrations (represented by the referencesignal x(n)) and the location of interference with the drive signal(y(n) from the adaptive filter w). The reference signal x(n) is inputinto the adaptive response filter w and this signal is modified byfilter coefficients of the w filter to provide the drive signal y(n). Inorder to compensate for the acoustic response of the sensor to theoutput of the secondary vibration source (termed C) in the conventionaltime domain adaptive control system an estimate of C is used to modifythe reference signal x(n) before it is input into the LMS algorithm. TheC coefficients provide a model of the delay and reverberant response ofthe system. For a multichannel system with m secondary vibration sourcesand l sensors, the coefficients of the adaptive response filter w shouldbe adjusted at every sample in the time domain according to thefollowing equation: ##EQU1## where μ is a convergence coefficient

e_(l) (n) is the sampled output from the l^(th) sensor

r_(lm) (n) is a sequence formed by filtering the reference signal x(n)by C which models the response of the l^(th) sensor to the output of them^(th) secondary vibration source.

This requires each reference signal to be filtered by a filter which hascoefficients for all the paths between the secondary vibrations sourcesand the sensors.

In the single channel embodiment shown in FIGS. 1a and 1b, the updaterequired for the w coefficients is determined in the frequency domainand implemented in the time domain. This is achieved by taking theFourier transform of the reference signal x(n) and the error signale(n). The Fourier transform of the error signal E_(k) is then convolvedwith the complex conjugate of the Fourier transform of the referencesignal X_(k) to form a cross spectral estimate. The inverse Fouriertransform of this cross spectral estimate is then taken to form a crosscorrelation estimate. The causal part of the cross correlation estimateis then used to update the coefficients of the adaptive response filterw.

In the above no consideration has been given to compensating for theresponse of the sensors to the secondary vibration sources. FIGS. 1a and1b show alternative methods for doing this. In FIG. 1a the Fouriertransform E_(k) of the error signal e(n) is multiplied by the complexconjugate of an estimate of the complex transfer function C for thek^(th) iteration. The result of this operation is then multiplied by thecomplex conjugate of the Fourier transform of the reference signal X_(k)to form the cross spectral estimate. Thus the update algorithm for theadaptive control system shown in FIG. 1a can be given by the followingequation:

    w(n+1)=w(n)-μIFFT X.sup.H.sub.k (C.sup.H E.sub.k)!

where

μ is a convergence coefficient,

X_(k) represents a vector of complex values of the Fourier transform ofthe reference signal x(n) at the k^(th) iteration

E_(k) represents a matrix of complex values of the Fourier transform ofthe error signals e(n) at the k^(th) iteration

C represents the matrix of transfer functions

H denotes the complex conjugate of the matrix

IFFT denotes the inverse fast Fourier transform of the term in thebrackets.

The convergence coefficient is provided to increase the stability of theadaptive control system and it is sufficiently small to smooth out theeffect of random errors in the cross spectral estimate on the adaption.Although in the above algorithm the convergence coefficient ismultiplied by the cross correlation estimate, the convergencecoefficient may equally be multiplied by the cross spectral estimate andthe algorithm is given by:

    w(n+1)=w(n)-IFFT  μX.sup.H.sub.k (C.sup.H E.sub.k)!

In the above equations the C matrix contains the transfer functions or amodel of the amplitude and phase change applied to each drive signal asdetected by each sensor, whereas the conjugate of the C matrixrepresents a model of the amplitude and the inverse of the phase.

Thus in the active vibration control system illustrated in FIG. 1a thereare three Fourier transform operations to be undertaken for the updatedata and the transform E_(k) of each error signal must be multiplied bythe conjugate of the transfer functions C for each path from a secondaryvibration source to an error sensor. The time taken for the calculationsin the arrangement shown in FIG. 1a are approximately proportional to(log₂ N×N)×(No. of error sensors×No. of secondary vibration sources). Ifthis is compared with the computational time of the conventional timedomain algorithm which is approximately proportional to N² ×(No. ofreferences×No. of error sensors×No. of secondary vibration sources), itcan be seen that even for a single channel system the control systemshown in FIG. 1a is more computationally efficient for an adaptiveresponse filter w having a number of taps of about 64 or greater. Thecomputation of the cross correlation estimate by firstly calculating thecross spectral estimate reduces the number of calculation steps requiredsince the formation of the cross correlation estimate in the time domainrequires the convolving of the reference and error signals, whereas inthe frequency domain the formation of the cross spectral estimate can beachieved merely by multiplying the functions.

Where advantages of the control system of FIG. 1 are fully utilised isin a multichannel system where a number of reference signals, a numberof secondary vibration sources and a number of error sensors areprovided. For the control system shown in FIG. 1a, each of the referencesignals does not have to be filtered by a model of the sensor responsesto the secondary vibration sources. This reduction in computation is inaddition to the computational saving discussed above for the singlechannel system.

FIG. 1b illustrates an alternative active vibration control systemaccording to one embodiment of the present invention. In thisarrangement the only difference is in the position of the estimate of C.Instead of multiplying the Fourier transform of the error signal by thecomplex conjugate of C, the Fourier transform of the reference signal ismultiplied by the matrix of transfer functions C. The cross spectralestimate is then formed by taking the complex conjugate of the result ofpassing the Fourier transform of the reference signal through the Cfilter and multiplying this complex conjugate with the Fourier transformof the error signal. The algorithm is given by:

    w(n+1)=w(n)-IFFT  (CX.sub.k).sup.H E.sub.k !

As for the arrangement shown in FIG. 1a, the cross correlation estimateis multiplied by a convergence coefficient μ in order to compensate forrandom errors. In a like manner to that shown in FIG. 1a the crossspectral estimate can alternatively be multiplied with the convergencecoefficient and then the algorithm is given by:

    w(n+1)=w(n)-IFFT  (CX.sub.k).sup.H E.sub.k !

For the arrangement shown in FIG. 1b, the computational efficiency forthe single channel system is the same as that of the arrangement shownin FIG. 1a. This control system also benefits from forming the crosscorrelation estimate by firstly forming the cross spectral estimate.When there is a single reference signal and a number of secondaryvibration sources and error sensors, the arrangement shown in FIG. 1b isequally as computationally efficient as the arrangement shown in FIG.1a. However, when more than one reference signal is used thecomputational efficiency of the arrangement shown in 1b compared to thearrangement shown in FIG. 1a decreases since it is approximatelyproportional to (log₂ N×N)×(No. of references×No. of error sensors×No.of secondary vibration sources). The number of filtering operations thatmust be carried out by the transfer function C is increased by a factorwhich is the number of reference signals.

FIGS. 1c, 1d and 1e illustrate three control systems with

1) two reference signals, one secondary vibration source and one errorsensor,

2) one reference signal, one secondary vibration source and two errorsensors, and

3) one reference signal, two secondary vibration sources and one errorsensor.

These three drawings illustrate how a multichannel system with a numberof references, secondary vibration sources and error sensors provide acomplex system with a matrix C of transfer functions, a number ofFourier transformed reference signals X_(k), and a number of Fouriertransformed error signals E_(k). The arrangements shown in FIGS. 1c, dand e are multichannel versions of the single channel system shown inFIG. 1a. A multichannel system of the model shown in FIG. 1b can bebuilt up in a like manner to that shown in FIGS. 1c, d and e as would beevident to a skilled person in the art.

In the multichannel system with a number of error sensors the algorithmreduces the noise by reducing the sum of the mean of the square of theerror signals in a similar manner to that disclosed in WO88/02912.

In addition to the modification of the filter coefficients to reduce thesum of the mean of the square of the error signals, the filtercoefficients can be modified to reduce the amplitude of portions of thedrive signals by a predetermined amount. This is termed "effortweighting" and can increase the stability of the algorithm as well asallow for selection of the effort taken to converge for signals ofdifferent delays or different frequencies dependent upon whether thefilter coefficients are weighted in the time or frequency domain.

So far in considering the way in which the algorithm works, noconsideration has been given to the practical considerations of takingthe Fourier transform of the continuous reference signal x(n) and errorsignal e(n). In order to perform a discrete fast Fourier transform ablock or window of data must be stored and operated on. The number ofdata points which are required must at least correspond to the delayassociated with the adaptive response filter w since for a referencesignal x(n) the effect on it by the w filter presented in the errorsignal e(n) must be present.

If the block of reference data has a number n of data points foroperation on by the Fourier transform then the n^(th) data point willhave a contribution in the error signal e(n) which is delayed by thelength of the w filter. Thus if a time aligned window of error data e(n)was taken, the delayed contributions from the n^(th) data point in thereference signal would not be measured. This reduces the possibility ofconvergence of the algorithm. This problem is overcome by taking a blockor window of data having n data points where the last few p data pointsare set to zero. Thus the block of data has a length of 0 to n but onlythe data points 0 to n-p contain actual reference signal data. Thenumber p of data points which are set to zero is dependent on the numberof tap delays of the w filter. The number p should be set to be at leastthe same number if not greater than the number of taps in the w filter.

Using this method assures that all contributions from the referencesignal data point x(n-p) are contained within the error signal datablock e(n) for the two time aligned blocks of data. FIG. 2 illustratesthe two data blocks for the reference and error signals. These blocks ofdata are used for the fast Fourier transform and this method ensuresthat all contributions from the reference signal data points are foundin the error signal data block.

The data blocks or windows represent "snap shots" in time of thereference and error signals. There is no requirement for these datablocks to be taken end to end. Blocks of data can be taken at intervalsof time. If the intervals between the acquisition of the data blocks islarge then clearly the adaption of the coefficients of the w filter willbe slow in response to rapidly changing conditions. However, for manypractical applications the update of the coefficients of the w filterneed not take place rapidly.

Thus because the adaption of the reference signal by the w filtercoefficients takes place in the time domain, the output drive signals toprovide the secondary vibrations are not delayed. Only the modificationof the filter coefficients of the w filter are delayed.

So far only the method of operation of the algorithm has beenconsidered. FIG. 3 illustrates the construction of a practical activevibration control system for use in a motor vehicle. FIG. 3 illustratesa multichannel system with four reference signal generators 31, fourerror sensors 42 and two secondary vibration sources 37. As mentionedhereinabove the present invention is particularly suited to amultichannel system having more than one reference signal since thisprovides for the greatest computational saving. In the arrangement shownin FIG. 3 the reference signal generators 31 comprise four transducerssuch as accelerometers placed on the suspension of the vehicle. Thesetransducers provide signals indicative of the vibrational noisetransmitted from the road wheel to the vehicle cabin. The outputs of thetransducers 31 are amplified by the amplifiers 32 and low pass filteredby the filters 33 in order to avoid aliasing. The reference signals arethen multiplexed by the multiplexer 34 and digitised using the analoguedigital converter 35. This provides reference signals x_(i) (n) to theprocessor 36 which is provided with memory 61.

Four error sensors 42 are provided within the vehicle cabin at spacelocations such as around the headlining. These microphones 42₁ through42₄ detect the noise within the cabin. The output of the microphones 42is then amplified by the amplifiers 43 and low pass filtered by the lowpass filters 44 in order to avoid aliasing. The output of the low passfilters 44 is then multiplexed by the multiplexer 45 before beingdigitised by the analogue to digital converter 46. The output of theanalogue digital converter e_(l) (n) is then input into the processor36.

Drive signals y_(m) (n) are output from the processor 36 and convertedto an analogue signal by the digital to analogue converter 41. Theoutput of the analogue to digital converter 41 is then demultiplexed bythe demultiplexer 38. The demultiplexer 38 separates the drive signalsinto separate drive signals for passage through low pass filters 39 inorder to remove high frequency digital sampling noise. The signal isthen amplified by the amplifiers 40 and output to the secondaryvibration sources 37₁ and 37₂ which comprise loudspeakers providedwithin the cabin of the vehicle. Conveniently, the loudspeakers cancomprise the loudspeakers of the in-car entertainment system of thevehicle. In such an arrangement the drive signals are mixed with thein-car entertainment signals for output by the loudspeakers, as isdisclosed in GB 2252657.

Thus the processor is provided with the reference signals x_(i) (n) andthe error signals e_(l) (n) and outputs the drive signals y_(m) (n) andis adapted to perform the algorithm as hereinbefore described.

Although in FIG. 3 the analogue to digital converters 35 and 46 and thedigital to analogue converter 41 are shown separately, such can beprovided by a single chip. FIG. 3 also shows the processor receiving aclock signal 60 from a sample rate oscillator 47. The processor thusoperates at a fixed frequency related to the frequencies of thevibrations to be reduced only by the requirement to meet Nyquist'scriterion. The processor 36 can be a fixed point processor such as theTMS 320 C50 processor available from Texas Instruments. Alternatively,the floating point processor TMS 320 C30 also available from TexasInstruments can be used to perform the algorithm.

Although the arrangement shown in FIG. 3 illustrates a system forcancelling road noise transmitted from the road wheel of a vehicle, thesystem can also be used for cancelling engine noise where a referencesignal is provided indicative of the noise generated by the engine of avehicle. In this instance only a single-reference signal is required andalthough the full potential computational saving of the algorithm is notutilised, the computational requirement is still reduced compared to theconventional time domain algorithm.

Further, although the secondary vibration sources illustrated in FIG. 3are loudspeakers they could alternatively be vibrators or a mix of both.

FIGS. 4a and 4b illustrate other embodiments of the present invention.In these embodiments adaption is performed in the frequency domain.FIGS. 4a and 4b differ from FIGS. 1a and 1b in that the w filtercoefficients are complex and require the input to the w filter to be thetransform of the reference signal. Also, there is no need to inversetransform the cross spectral estimate to modify the complex filtercoefficients. The output of the w filter must be inverse transformed togenerate the drive signal y(n) since the w filter acts on the amplitudeand phase of spectral components.

For the arrangement in FIG. 4a the algorithm can be given by

    w.sub.k+1 =w.sub.k -μX.sup.H.sub.k (C.sup.H E.sub.k)

whereas for FIG. 4b the algorithm can be given by

    w.sub.k+1 =w.sub.k -μ(CX.sub.k).sup.H E.sub.k

As for the arrangements shown in FIGS. 1a and 1b in order to avoid theproblem of the window of error data not containing the contribution fromthe reference data in a time aligned window, the latter part of theerror data block is zeroed in the manner described with respect to FIG.2 with all the associated advantages.

Although the embodiments of the invention described hereinabove havebeen described with reference to an active vibration control system thepresent invention is not limited thereto. The present invention appliesto the reduction of any undesired signals. A signal indicative of atleast selected undesired vibrations from a vibration source is used toprovide a drive signal to cancel the undesired vibrations at a location.The degree of success in reducing the undesired vibrations is measuredto provide a residual signal and this is used to adjust the drive signalto provide better cancellation. Thus the undesired signals beingcancelled could be electrical or acoustic.

I claim:
 1. An adaptive control system for reducing undesired signals,comprising signal means to provide at least one first signal indicativeof at least some of the undesired signals; processing means whichprocesses said at least one first signal to provide at least onesecondary signal to interfere with the undesired signals; and residualmeans to provide for said processing means at least one residual signalindicative of the interference between said undesired and secondarysignals; wherein said processing means comprises: means for transformingsaid at least one first signal and said at least one residual signal toprovide the amplitude and phase of spectral components of said signal;means for collating the transformed signals; means for inversetransforming of the outcome of said collation; adaptive response filtermeans having filter coefficients which filters the at least one firstsignal in providing the at least one secondary signal; means foradapting said filter coefficients to reduce each residual signal, whichmeans for adapting adapts said filter coefficients using said inversetransform of the outcome of the collation; wherein: said means forcollating said transformed signals has means for forming at least onecross spectral estimate; said means for inverse transforming of theoutcome of the collation inverse transforms said at least one crossspectral estimate to form at least one cross correlation estimate; andsaid means for adapting the filter coefficients of said adaptiveresponse filter means uses said at least one cross correlation estimatewhen adapting the filter coefficients.
 2. An adaptive control system asclaimed in claim 1 wherein said processing means comprises means fordigitally sampling said at least one first signal and said at least oneresidual signal; means for storing a first plurality of digits for eachsignal which forms first signal and residual signal data blocksrespectively; and means for time aligning said first signal data blocksand said residual signal data blocks; said processing means furthercomprising means for setting a second plurality of said digits at theend of each first signal data block to zero and thereby forming amodified first signal data block; and means for transforming themodified first signal data block and the time aligned residual signaldata block to use in the collation.
 3. An adaptive control system asclaimed in claim 2, wherein said means for setting the second pluralityof said digits at the end of each first signal data block to zerooperates in dependence upon a delay between the first signal and thecontribution from the first signal in the residual signal; and saidprocessing means for setting a number of said digits at the end of eachfirst signal data block to zero comprises means for selecting a numberof digits to set to zero such that the time taken to sample said numberis greater than the delay experienced by a signal passing through saidadaptive response filter means.
 4. An adaptive control system as claimedin claim 1, wherein said means for forming the cross spectral estimatehas means for multiplying a complex conjugate of the transform of thefirst signal with the transform of the residual signal.
 5. An adaptivecontrol system as claimed in claim 1, wherein said processing means hasmeans for multiplying said at least one cross spectral estimate with aconvergence coefficient to reduce the effect of random errors in thecross spectral estimate on the filtering of the at least one firstsignal.
 6. An adaptive control system as claimed in claim 1, whereinsaid processing means has means for multiplying said at least one cresscorrelation estimate with a convergence coefficient to reduce the effectof random errors in the cross correlation estimate on the filtering ofthe at least one first signal.
 7. An adaptive control system as claimedin claim 1, wherein said processing means further includes systemresponse filter means to model the response of said residual means to atleast one secondary signal and said system response filter meanscomprises complex filter coefficients which represent the frequencyresponse of said residual means to at least one said secondary signal,and said processing means has means for filtering the transform of saidat least one first signal using said complex filter coefficients.
 8. Anadaptive control system as claimed in claim 1, wherein said processingmeans further includes system response filter means comprising complexfilter coefficients which represent the amplitude and the inverse of thephase of the frequency response of said residual means to at least onesaid secondary signal, and said processing means has means for filteringthe transform of said at least one residual signal using said complexfilter coefficients.
 9. An adaptive control system as claimed in claim1, wherein said means for adapting said filter coefficients operates toreduce the amplitude of each secondary signal.
 10. An adaptive controlsystem as claimed in claim 1, wherein said residual means provides aplurality of residual signals; and said means for adapting said filtercoefficients of said adaptive response filter operates to reduce the sumof the mean of the square of the residual signals.
 11. An adaptivecontrol system as claimed in claim 1, wherein: said undesired signalscomprise undesired acoustic vibrations; said adaptive control systemcomprises at least one secondary vibration source responsive to said atleast one secondary signal to provide secondary vibrations to interferewith said undesired acoustic vibrations; said residual means comprisesat least one sensor means which senses the residual vibrations resultingfrom the interference between said undesired acoustic vibrations andsaid secondary vibrations and provides said at least one residualsignal.
 12. A method of actively reducing undesired signals comprisingthe steps of: providing at least one first signal indicative of at leastsome of the undesired signals; using said at least one first signal toprovide at least one secondary signal to interfere with said undesiredsignals; providing at least one residual signal indicative of theinterference between said undesired and secondary signals; transformingsaid at least one first signal and said at least one residual signal toprovide the amplitude and phase of spectral components of said signals;collating the transformed signals; inverse transforming the outcome ofthe collation; filtering said at least one secondary signal using filtercoefficients in an adaptive response filter means to reduce the residualsignals; adapting the filter coefficients using said inverse transformof the outcome of the collation; wherein the transformed signals arecollated by forming at least one cross spectral estimate; said at leastone cross spectral estimate is inverse transformed to form at least onecross correlation estimate; and said means for adapting said filtercoefficients uses said at least one cross correlation.
 13. A method asclaimed in claim 12, wherein said at least one first signal and said atleast one residual signal are digitally sampled, including the steps of:storing in electronic memory means a first plurality of digits for eachsaid signal to form first signal data blocks and residual signal datablocks respectively; time aligning said first signal data blocks andresidual signal data blocks; setting a second plurality of said digitsat the end of each first signal data block to zero to form a modifiedfirst signal data block; and transforming the modified first signalblock and the time aligned residual signal data block for use in thecollation.
 14. A method as claimed in claim 13, wherein the secondplurality of digits at the end of each modified first signal data blockwhich is set to zero are selected in dependence upon the delay betweenthe first signal and the contribution from the first signal in theresidual signal, and the number of digits set to zero is determined tobe at least the same number as the number of taps in the adaptive filtermeans such that the time taken to sample said number is greater than thedelay experienced by a signal during filtering of the at least one firstsignal.
 15. A method as claimed in claim 12, wherein the cross spectralestimate is formed by multiplying the complex conjugate of the transformof the first signal with the transform of the residual signal.
 16. Amethod as claimed in claim 12, wherein the cross spectral estimate ismultiplied with a convergence coefficient to reduce the effect of randomerrors in the cross spectral estimate on the filtering of the the atleast one first signal.
 17. A method as claimed in, claim 12, whereinthe cross correlation estimate is multiplied with a convergencecoefficient to reduce the effects of random errors in the crosscorrelation estimate on the filtering of the at least one first signal.18. A method as claimed in claim 12, wherein the response of said atleast one residual signal to said at least one secondary signal ismodelled by system response filter means, and said system responsefilter means has complex filter coefficients which represent thefrequency response of said at least one residual signal to at least onesaid secondary signal, said method including the steps of multiplyingthe said transform of said at least one first signal with said complexfilter coefficients.
 19. A method as claimed in claim 12, including thestep of filtering the transform of said at least one residual signalusing system response filter means which comprises complex filtercoefficients which represent the amplitude and the inverse of the phaseof the frequency response of said sensed residual vibration to said atleast one secondary signal.
 20. A method as claimed in claim 12,including the step of adapting said filter coefficients to reduce theamplitude of each secondary signal.
 21. A method as claimed in claim 12,including the steps of using sensor means to sense residual signals in aplurality of locations to provide a plurality of residual signals andadapting said filter coefficients to reduce the sum of the square of theresidual signals.
 22. A method as claimed in claim 12, wherein saidundesired signals comprise undesired acoustic vibrations, the methodcomprising the steps of: converting said at least one secondary signalinto at least one secondary vibration using vibration means, the atleast one secondary vibration interfering with said undesiredvibrations: and using sensor means to sense the residual vibrationsresulting from the interference between said undesired and secondaryvibrations and to provide said residual signal.
 23. An adaptive controlsystem for reducing undesired signals, comprising signal means toprovide at least one first signal indicative of at least some of theundesired signals; processing means which processes said at least onefirst signal to produce at least one secondary signal to interfere withthe undesired signals; and residual means to provide for said processingmeans at least one residual signal indicative of the interferencebetween said undesired and secondary signals; wherein said processingmeans comprises means for digitally sampling said at least one firstsignal and said at least one residual signal; means for storing a firstplurality of digits for each said signal to form first signal andresidual signal data blocks respectively; means for setting a secondplurality of said digits at the end of each first signal data block tozero to form a modified first signal data block; means for transformingthe modified first signal data block and the residual signal data blockto provide the amplitude and phase of spectral components of saidsignals; means for transforming the at least one first signal to providethe amplitude and phase of spectral components of said signal; adaptiveresponse filter means which filters the transformed first signal usingcomplex filter coefficients in the provision of each secondary signal;and means for inverse transforming the filtered transformed first signalin the provision of said at least one secondary signal; wherein saidprocessing means has means for forming at least one cross spectralestimate using the transforms of said at least one modified first signaldata block and said at least one residual signal data block; and meansfor adapting the filter coefficients using said at least one crossspectral estimate.
 24. An adaptive control system as claimed in claim23, wherein said processing means has means for setting the secondplurality of said digits at the end of each modified first signal datablock to zero which operates in dependence upon a delay between thefirst signal and the contribution from the first signal in the residualsignal, and has means for selecting the number of digits to set to zerosuch that the time taken to sample said number is greater than the delayexperienced by a signal passing through said adaptive response filtermeans.
 25. An adaptive control system as claimed in claim 23, whereinsaid means forming the cross spectral estimate multiplies a complexconjugate of the transform of the first signal with the transform of theresidual signal.
 26. An adaptive control system as claimed in claim 23,wherein said processing means has means for multiplying said at leastone cross spectral estimate with a convergence coefficient to reduce theeffect of random errors in the cross spectral estimate on the filteringof the at least one first signal.
 27. An adaptive control system asclaimed in claim 23, wherein said processing means further includessystem response filter means to model the response of said residualmeans to at least one secondary signal and said system response filtermeans comprises complex filter coefficients which represent thefrequency response of said residual means to at least one said secondarysignal, and said system response filter means filters the transform ofsaid at least one first signal using said complex filter coefficients.28. An adaptive control system as claimed in claim 23, wherein saidprocessing means further includes system response filter meanscomprising complex filter coefficients which represent the amplitude andthe inverse of the phase of the frequency response of said residualmeans to at least one said secondary signal, and said system responsefilter means for filtering the transform of said at least one residualsignal using said complex filter coefficients.
 29. An adaptive controlsystem as claimed in claim 23, wherein said means for adapting saidfilter coefficients reduces the amplitude of each secondary signal. 30.An adaptive control system as claimed in claim 23, wherein said residualmeans provides a plurality of residual signals, and said means foradapting said filter coefficients of said adaptive response filterreduces the sum of the mean of the square of the residual signals. 31.An adaptive control system as claimed in claim 23, wherein: saidundesired signals comprise undesired acoustic vibrations; said adaptivecontrol system comprises at least one secondary vibration sourceresponsive to said at least one secondary signal to provide secondaryvibrations to interfere with said undesired acoustic vibrations; saidresidual means comprises at least one sensor means which senses theresidual vibrations resulting from the interference between saidundesired acoustic vibrations and said secondary vibrations and whichprovides said at least one residual signal.
 32. A method of activelyreducing undesired signals comprising the steps of using sensor means tosense undesired signals and to provide at least one first signalindicative of at least some of the undesired signals; using said atleast one first signal to provide at least one secondary signal tointerfere with said undesired signals; using residual means to provideat least one residual signal indicative of the interference between saidundesired and secondary signals; digitally sampling said at least onefirst signal and said at least one residual signal; storing a firstplurality of digits for each said signal to form first signal andresidual signal data blocks; time aligning said first signal andresidual signal data blocks; setting a second plurality of said digitsat the end of each first signal data block to zero to form a modifiedfirst signal data block; transforming the modified first signal datablock to provide the amplitude and phase of spectral components of saidsignals; transforming the at least one first signal to provide theamplitude and phase of spectral components of said signal; filtering thetransformed at least one first signal using complex filter coefficientsin an adaptive response filter means; inverse transforming the filteredtransform of the at least one first signal in provision of said at leastone secondary signal; wherein at least one cross spectral estimate isformed using the transform of said at least one modified first signaldata block and said at least one residual signal data block; and thecomplex filter coefficients are adapted using said at least one crossspectral estimate.
 33. A method as claimed in claim 32, wherein thesecond plurality of digits at the end of each modified first signal datablock which are set to zero are selected in dependence upon the delaybetween the first signal and the contribution from the first signal inthe residual signal, the selection of digits set to zero beingdetermined so that the number of digits set to zero is at least the samenumber as the number of taps in the adaptive filter means, such that thetime taken to sample said number is greater than the delay experiencedby a signal during adjustment of the or each secondary signal.
 34. Amethod as claimed in claim 32, wherein the cross spectral estimate isformed by multiplying the complex conjugate of the transform of thefirst signal with the transform of the residual signal.
 35. A method asclaimed in claim 32, wherein the cross spectral estimate is multipliedwith a convergence coefficient to reduce the effect of random errors inthe cross spectral estimate on the filtering of the at least one firstsignal.
 36. A method as claimed in claim 32, wherein the response ofsaid at least one residual signal to said at least one secondary signalis modelled by system response filter means and said system responsefilter means has complex filter coefficients which represent thefrequency response of said at least one residual signal to at least onesaid secondary signal, said method including the step of multiplying thesaid transform of said at least one first signal with said complexfilter coefficients.
 37. A method as claimed in claim 32, including thestep of filtering the transform of said at least one residual signalusing system response filter means which comprises complex filtercoefficients which represent the amplitude and the inverse of the phaseof the frequency response of said sensed residual vibration to said atleast one secondary signal.
 38. A method as claimed in claim 32,including the step of adapting said filter coefficients to reduce theamplitude of each secondary signal.
 39. A method as claimed in claim 32,including the steps of using sensor means to sense residual signals in aplurality of locations to provide a plurality of residual signal andadapting said filter coefficients to reduce the sum of the square of theresidual signals.
 40. A method as claimed in claim 32, wherein saidundesired signals comprise undesired acoustic vibrations, the methodcomprising the steps of converting said at least one secondary signal toat least one secondary vibration using vibration means, the at least onesecondary vibration interfering with said undesired vibrations, andsensing the residual vibrations resulting from the interference betweensaid undesired and secondary vibrations to provide said residual signal.